Genetic Bi-objective Optimization Approach to Habitability Score

TL;DR

This paper employs genetic algorithms to optimize the Cobb-Douglas Habitability Score for exoplanets, extending to multi-objective optimization to better classify planetary habitability.

Contribution

It introduces a genetic algorithm-based method for optimizing habitability scores, including a bi-objective formulation, to improve exoplanet classification accuracy.

Findings

Genetic algorithms effectively optimize habitability scores.

The bi-objective approach enhances classification robustness.

Comparison with benchmarks validates the method's effectiveness.

Abstract

The search for life outside the Solar System is an endeavour of astronomers all around the world. With hundreds of exoplanets being discovered due to advances in astronomy, there is a need to classify the habitability of these exoplanets. This is typically done using various metrics such as the Earth Similarity Index or the Planetary Habitability Index. In this paper, Genetic Algorithms are used to evaluate the best possible habitability scores using the Cobb-Douglas Habitability Score. Genetic Algorithm is a classic evolutionary algorithm used for solving optimization problems. It is based on Darwin's theory of evolution, "Survival of the fittest". The working of the algorithm is established through comparison with various benchmark functions and extended its functionality to Multi-Objective optimization. The Cobb-Douglas Habitability Function is formulated as a bi-objective as well as a single objective optimization problem to find the optimal values to maximize the Cobb-Douglas Habitability Score for a set of promising exoplanets.